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This article is part of the supplement: First International Workshop on Text Mining in Bioinformatics (TMBio) 2006

Open Access Proceedings

A comparison study on algorithms of detecting long forms for short forms in biomedical text

Manabu Torii1, Zhang-zhi Hu2, Min Song3, Cathy H Wu2 and Hongfang Liu1*

Author Affiliations

1 Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, 4000 Resevoir Rd, NW, Washington, DC 20057, USA

2 Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven St., NW, Washington, DC 20007, USA

3 Department of Information Systems, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA

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BMC Bioinformatics 2007, 8(Suppl 9):S5  doi:10.1186/1471-2105-8-S9-S5

Published: 27 November 2007

Abstract

Motivation

With more and more research dedicated to literature mining in the biomedical domain, more and more systems are available for people to choose from when building literature mining applications. In this study, we focus on one specific kind of literature mining task, i.e., detecting definitions of acronyms, abbreviations, and symbols in biomedical text. We denote acronyms, abbreviations, and symbols as short forms (SFs) and their corresponding definitions as long forms (LFs). The study was designed to answer the following questions; i) how well a system performs in detecting LFs from novel text, ii) what the coverage is for various terminological knowledge bases in including SFs as synonyms of their LFs, and iii) how to combine results from various SF knowledge bases.

Method

We evaluated the following three publicly available detection systems in detecting LFs for SFs: i) a handcrafted pattern/rule based system by Ao and Takagi, ALICE, ii) a machine learning system by Chang et al., and iii) a simple alignment-based program by Schwartz and Hearst. In addition, we investigated the conceptual coverage of two terminological knowledge bases: i) the UMLS (the Unified Medical Language System), and ii) the BioThesaurus (a thesaurus of names for all UniProt protein records). We also implemented a web interface that provides a virtual integration of various SF knowledge bases.

Results

We found that detection systems agree with each other on most cases, and the existing terminological knowledge bases have a good coverage of synonymous relationship for frequently defined LFs. The web interface allows people to detect SF definitions from text and to search several SF knowledge bases.

Availability

The web site is http://gauss.dbb.georgetown.edu/liblab/SFThesaurus webcite.